Machine-learning study using improved correlation configuration and application to quantum Monte Carlo simulation
Machine-learning study using improved correlation configuration and application to quantum Monte Carlo simulation
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使用改进的相关配置的机器学习研究及其在量子蒙特卡罗模拟中的应用
DOI：
10.1103/physreve.102.021302
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发表时间：
2020
影响因子：
2.4
通讯作者：
Lee Hwee Kuan
中科院分区：
文献类型：
--
作者：
Tomita Yusuke;Shiina Kenta;Okabe Yutaka;Lee Hwee Kuan
在自旋模型相分类的机器学习研究中，我们使用基于Fortuin-Kasteleyn表示的相关构型的改进估计器作为普通相关构型的替代。利用改进的估计器对经典自旋模型的相位进行了分类，并将该方法应用于循环算法的量子蒙特卡罗模拟中。分析了正方形晶格上自旋1/2量子模型的Berezinskii-Kosterlitz-Thouless(BKT)相变。我们用机器学习的方法对量子模型的BKT相和顺磁相进行了分类。我们证明了利用经典模型的训练数据可以对量子模型进行分类。
We use the Fortuin-Kasteleyn representation-based improved estimator of the correlation configuration as an alternative to the ordinary correlation configuration in the machine-learning study of the phase classification of spin models. The phases of classical spin models are classified using the improved estimators, and the method is also applied to the quantum Monte Carlo simulation using the loop algorithm. We analyze the Berezinskii-Kosterlitz-Thouless (BKT) transition of the spin-1/2 quantummodel on the square lattice. We classify the BKT phase and the paramagnetic phase of the quantummodel using the machine-learning approach. We show that the classification of the quantummodel can be performed by using the training data of the classicalmodel.